Towards Dynamic Causal Discovery with Rare Events: A Nonparametric Conditional Independence Test
Causal phenomena associated with rare events occur across a wide range of engineering problems, such as risk-sensitive safety analysis, accident analysis and prevention, and extreme value theory. However, current methods for causal discovery are often unable to uncover causal links, between random variables in a dynamic setting, that manifest only when the variables first experience low-probability realizations. To address this issue, we introduce a novel statistical independence test on data collected from time-invariant dynamical systems in which rare but consequential events occur. In particular, we exploit the time-invariance of the underlying data to construct a superimposed dataset of the system state before rare events happen at different timesteps. We then design a conditional independence test on the reorganized data. We provide non-asymptotic sample complexity bounds for the consistency of our method, and validate its performance across various simulated and real-world datasets, including incident data collected from the Caltrans Performance Measurement System (PeMS). Code containing the datasets and experiments is publicly available.
Code (1)
Tasks
Causal DiscoveryMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Formalizing and falsifying causal pathways of rare events
Building on recent formalizations of root cause analysis for rare events (``outliers'') in structural equation models, we propose a formal definition of a causal pathway and discuss its testable implications. We identify…
Identification of Nonparametric Dynamic Causal Structure and Latent Process in Climate System
The study of learning causal structure with latent variables has advanced the understanding of the world by uncovering causal relationships and latent factors, e.g., Causal Representation Learning (CRL). However, in real…
Causal DiscoveryRepresentation LearningFast Nonparametric Conditional Independence Testing via Two-Stage Regression
Constraint-based causal discovery relies on repeated conditional independence tests, but fast nonparametric tests often sacrifice calibration, especially when variables depend on the conditioning set through nonlinear re…
From Causal Discovery to Dynamic Causal Inference in Neural Time Series
Time-varying causal models provide a powerful framework for studying dynamic scientific systems, yet most existing approaches assume that the underlying causal network is known a priori - an assumption rarely satisfied i…
Causal InferenceCausal inference for the expected number of recurrent events in the presence of a terminal event
We study causal inference and efficient estimation for the expected number of recurrent events in the presence of a terminal event. We define our estimand as the vector comprising both the expected number of recurrent ev…
Causal Inference